Articles | Volume 17, issue 9
https://doi.org/10.5194/nhess-17-1683-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/nhess-17-1683-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Multi-variable flood damage modelling with limited data using supervised learning approaches
Dennis Wagenaar
CORRESPONDING AUTHOR
Deltares, Boussinesqweg 1, 2629 HV, Delft, the Netherlands
Jurjen de Jong
Deltares, Boussinesqweg 1, 2629 HV, Delft, the Netherlands
Laurens M. Bouwer
Deltares, Boussinesqweg 1, 2629 HV, Delft, the Netherlands
Viewed
Total article views: 4,644 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 12 Jan 2017)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
2,345 | 2,147 | 152 | 4,644 | 194 | 116 | 131 |
- HTML: 2,345
- PDF: 2,147
- XML: 152
- Total: 4,644
- Supplement: 194
- BibTeX: 116
- EndNote: 131
Total article views: 3,145 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 29 Sep 2017)
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
1,829 | 1,182 | 134 | 3,145 | 194 | 108 | 108 |
- HTML: 1,829
- PDF: 1,182
- XML: 134
- Total: 3,145
- Supplement: 194
- BibTeX: 108
- EndNote: 108
Total article views: 1,499 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 12 Jan 2017)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
516 | 965 | 18 | 1,499 | 8 | 23 |
- HTML: 516
- PDF: 965
- XML: 18
- Total: 1,499
- BibTeX: 8
- EndNote: 23
Viewed (geographical distribution)
Total article views: 4,644 (including HTML, PDF, and XML)
Thereof 4,362 with geography defined
and 282 with unknown origin.
Total article views: 3,145 (including HTML, PDF, and XML)
Thereof 2,968 with geography defined
and 177 with unknown origin.
Total article views: 1,499 (including HTML, PDF, and XML)
Thereof 1,394 with geography defined
and 105 with unknown origin.
Country | # | Views | % |
---|
Country | # | Views | % |
---|
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1
1
Cited
65 citations as recorded by crossref.
- Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model X. Fu et al. 10.3389/feart.2022.1102802
- Empirical flash flood vulnerability functions for residential buildings C. Arrighi et al. 10.1007/s42452-020-2696-1
- Augmenting a socio-hydrological flood risk model for companies with process-oriented loss estimation L. Schoppa et al. 10.1080/02626667.2022.2095207
- A Novel Estimation of the Composite Hazard of Landslides and Flash Floods Utilizing an Artificial Intelligence Approach M. Wahba et al. 10.3390/w15234138
- Testing empirical and synthetic flood damage models: the case of Italy M. Amadio et al. 10.5194/nhess-19-661-2019
- A probabilistic approach to estimating residential losses from different flood types D. Paprotny et al. 10.1007/s11069-020-04413-x
- Characterization of damages in buildings after floods in Vega Baja County (Spain) in 2019. The case study of Almoradí municipality R. Moya Barbera et al. 10.1016/j.cscm.2024.e03004
- Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast Texas W. Mobley et al. 10.5194/nhess-21-807-2021
- A comparison of building value models for flood risk analysis V. Röthlisberger et al. 10.5194/nhess-18-2431-2018
- Probabilistic Assessment of Pluvial Flood Risk Across 20 European Cities: A Demonstrator of the Copernicus Disaster Risk Reduction Service for Pluvial Flood Risk in Urban Areas A. Essenfelder et al. 10.1142/S2382624X22400070
- Evaluation of residential building damage for the July 2021 flood in Westport, New Zealand R. Paulik et al. 10.1186/s40562-024-00323-z
- Leveraging data driven approaches for enhanced tsunami damage modelling: Insights from the 2011 Great East Japan event M. Di Bacco et al. 10.1016/j.envsoft.2022.105604
- A generic physical vulnerability model for floods: review and concept for data-scarce regions M. Malgwi et al. 10.5194/nhess-20-2067-2020
- Are flood damage models converging to “reality”? Lessons learnt from a blind test D. Molinari et al. 10.5194/nhess-20-2997-2020
- Efficient building damage assessment from post-disaster aerial video using lightweight deep learning models C. Liu et al. 10.1080/01431161.2023.2277163
- Assessment of microscale economic flood losses in urban and agricultural areas: case study of the Santa Bárbara River, Ecuador J. Pinos et al. 10.1007/s11069-020-04084-8
- Advancing flood damage modeling for coastal Alabama residential properties: A multivariable machine learning approach M. Museru et al. 10.1016/j.scitotenv.2023.167872
- Leveraging machine learning for predicting flash flood damage in the Southeast US A. Alipour et al. 10.1088/1748-9326/ab6edd
- Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning X. Jiang et al. 10.1016/j.isprsjprs.2021.05.019
- Estimating exposure of residential assets to natural hazards in Europe using open data D. Paprotny et al. 10.5194/nhess-20-323-2020
- The role of socio-economic and property variables in the establishment of flood depth-damage curve for the data-scarce area in Malaysia S. Sulong & N. Romali 10.1080/1573062X.2022.2099292
- A new framework for flood damage assessment considering the within-event time evolution of hazard, exposure, and vulnerability T. Lazzarin et al. 10.1016/j.jhydrol.2022.128687
- Machine learning models to predict myocardial infarctions from past climatic and environmental conditions L. Marien et al. 10.5194/nhess-22-3015-2022
- Probabilistic Flood Loss Models for Companies L. Schoppa et al. 10.1029/2020WR027649
- Flood depth-damage and fragility functions derived with structured expert judgment G. Pita et al. 10.1016/j.jhydrol.2021.126982
- Bayesian Data-Driven approach enhances synthetic flood loss models N. Sairam et al. 10.1016/j.envsoft.2020.104798
- Are OpenStreetMap building data useful for flood vulnerability modelling? M. Cerri et al. 10.5194/nhess-21-643-2021
- Evaluating the spatial application of multivariable flood damage models R. Paulik et al. 10.1111/jfr3.12934
- Regional and Temporal Transferability of Multivariable Flood Damage Models D. Wagenaar et al. 10.1029/2017WR022233
- Flood Vulnerability Models and Household Flood Damage Mitigation Measures: An Econometric Analysis of Survey Data T. Endendijk et al. 10.1029/2022WR034192
- An empirical flood fatality model for Italy using random forest algorithm M. Yazdani et al. 10.1016/j.ijdrr.2023.104110
- A Consistent Approach for Probabilistic Residential Flood Loss Modeling in Europe S. Lüdtke et al. 10.1029/2019WR026213
- Expert-based versus data-driven flood damage models: A comparative evaluation for data-scarce regions M. Malgwi et al. 10.1016/j.ijdrr.2021.102148
- Invited perspectives: How machine learning will change flood risk and impact assessment D. Wagenaar et al. 10.5194/nhess-20-1149-2020
- Construction of flood loss function for cities lacking disaster data based on three-dimensional (object-function-array) data processing H. Lv et al. 10.1016/j.scitotenv.2021.145649
- A PCA spatial pattern based artificial neural network downscaling model for urban flood hazard assessment J. Carreau & V. Guinot 10.1016/j.advwatres.2020.103821
- Uncovering Drivers of Atmospheric River Flood Damage Using Interpretable Machine Learning C. Bowers et al. 10.1061/NHREFO.NHENG-1995
- The potential of open-access data for flood estimations: uncovering inundation hotspots in Ho Chi Minh City, Vietnam, through a normalized flood severity index L. Scheiber et al. 10.5194/nhess-23-2313-2023
- Evaluating adaptation measures for reducing flood risk: A case study in the city of Colombo, Sri Lanka D. Wagenaar et al. 10.1016/j.ijdrr.2019.101162
- Spatial Transferability of Residential Building Damage Models between Coastal and Fluvial Flood Hazard Contexts R. Paulik et al. 10.3390/jmse11101960
- The construction of flood loss ratio function in cities lacking loss data based on dynamic proportional substitution and hierarchical Bayesian model H. Lv et al. 10.1016/j.jhydrol.2020.125797
- Systemic Financial Risk Arising From Residential Flood Losses H. Thomson et al. 10.1029/2022EF003206
- The value of multi-source data for improved flood damage modelling with explicit input data uncertainty treatment: INSYDE 2.0 M. Di Bacco et al. 10.5194/nhess-24-1681-2024
- A machine learning-based prediction and analysis of flood affected households: A case study of floods in Bangladesh K. Ganguly et al. 10.1016/j.ijdrr.2018.12.002
- Flood Damage Assessment: A Review of Microscale Methodologies for Residential Buildings O. Aribisala et al. 10.3390/su142113817
- Enhancing resilience: Understanding the impact of flood hazard and vulnerability on business interruption and losses T. Endendijk et al. 10.1016/j.wre.2024.100244
- A Bayesian network approach for multi-sectoral flood damage assessment and multi-scenario analysis R. Harris et al. 10.1016/j.crm.2022.100410
- Understanding flood risk in urban environments: spatial analysis of building vulnerability and hazard areas in the Lisbon metropolitan area P. Santos et al. 10.1007/s11069-024-06731-w
- A data-mining approach towards damage modelling for El Niño events in Peru F. Brill et al. 10.1080/19475705.2020.1818636
- Impact of an Integrated Approach in Disaster Management O. Njoku et al. 10.4018/IJOCI.2020040102
- Improving flood impact estimations T. Sieg & A. Thieken 10.1088/1748-9326/ac6d6c
- Flood loss estimation using 3D city models and remote sensing data K. Schröter et al. 10.1016/j.envsoft.2018.03.032
- Flood Damage Analysis Using Machine Learning Techniques . Snehil & R. Goel 10.1016/j.procs.2020.06.011
- Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes N. Sairam et al. 10.1029/2019WR025068
- Validation of flood risk models: Current practice and possible improvements D. Molinari et al. 10.1016/j.ijdrr.2018.10.022
- Optimal Domain Scale for Stochastic Urban Flood Damage Assessment Considering Triple Spatial Uncertainties H. Lv et al. 10.1029/2021WR031552
- Probabilistic framework for quantifying infrastructure systems’ resilience against floods A. Daneshifar & H. Kashani 10.1080/23789689.2024.2328977
- Residential building and sub-building level flood damage analysis using simple and complex models R. Paulik et al. 10.1007/s11069-024-06756-1
- Preface: Damage of natural hazards: assessment and mitigation H. Kreibich et al. 10.5194/nhess-19-551-2019
- Flood Risk Modeling under Uncertainties: The Case Study of Croatia T. Kekez et al. 10.3390/w14101585
- Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates V. Rözer et al. 10.1029/2018EF001074
- A method to reconstruct flood scenarios using field interviews and hydrodynamic modelling: application to the 2017 Suleja and Tafa, Nigeria flood M. Malgwi et al. 10.1007/s11069-021-04756-z
- Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction D. Wagenaar et al. 10.1111/risa.13575
- Seamless Estimation of Hydrometeorological Risk Across Spatial Scales T. Sieg et al. 10.1029/2018EF001122
- Testing empirical and synthetic flood damage models: the case of Italy M. Amadio et al. 10.5194/nhess-19-661-2019
64 citations as recorded by crossref.
- Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model X. Fu et al. 10.3389/feart.2022.1102802
- Empirical flash flood vulnerability functions for residential buildings C. Arrighi et al. 10.1007/s42452-020-2696-1
- Augmenting a socio-hydrological flood risk model for companies with process-oriented loss estimation L. Schoppa et al. 10.1080/02626667.2022.2095207
- A Novel Estimation of the Composite Hazard of Landslides and Flash Floods Utilizing an Artificial Intelligence Approach M. Wahba et al. 10.3390/w15234138
- Testing empirical and synthetic flood damage models: the case of Italy M. Amadio et al. 10.5194/nhess-19-661-2019
- A probabilistic approach to estimating residential losses from different flood types D. Paprotny et al. 10.1007/s11069-020-04413-x
- Characterization of damages in buildings after floods in Vega Baja County (Spain) in 2019. The case study of Almoradí municipality R. Moya Barbera et al. 10.1016/j.cscm.2024.e03004
- Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast Texas W. Mobley et al. 10.5194/nhess-21-807-2021
- A comparison of building value models for flood risk analysis V. Röthlisberger et al. 10.5194/nhess-18-2431-2018
- Probabilistic Assessment of Pluvial Flood Risk Across 20 European Cities: A Demonstrator of the Copernicus Disaster Risk Reduction Service for Pluvial Flood Risk in Urban Areas A. Essenfelder et al. 10.1142/S2382624X22400070
- Evaluation of residential building damage for the July 2021 flood in Westport, New Zealand R. Paulik et al. 10.1186/s40562-024-00323-z
- Leveraging data driven approaches for enhanced tsunami damage modelling: Insights from the 2011 Great East Japan event M. Di Bacco et al. 10.1016/j.envsoft.2022.105604
- A generic physical vulnerability model for floods: review and concept for data-scarce regions M. Malgwi et al. 10.5194/nhess-20-2067-2020
- Are flood damage models converging to “reality”? Lessons learnt from a blind test D. Molinari et al. 10.5194/nhess-20-2997-2020
- Efficient building damage assessment from post-disaster aerial video using lightweight deep learning models C. Liu et al. 10.1080/01431161.2023.2277163
- Assessment of microscale economic flood losses in urban and agricultural areas: case study of the Santa Bárbara River, Ecuador J. Pinos et al. 10.1007/s11069-020-04084-8
- Advancing flood damage modeling for coastal Alabama residential properties: A multivariable machine learning approach M. Museru et al. 10.1016/j.scitotenv.2023.167872
- Leveraging machine learning for predicting flash flood damage in the Southeast US A. Alipour et al. 10.1088/1748-9326/ab6edd
- Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning X. Jiang et al. 10.1016/j.isprsjprs.2021.05.019
- Estimating exposure of residential assets to natural hazards in Europe using open data D. Paprotny et al. 10.5194/nhess-20-323-2020
- The role of socio-economic and property variables in the establishment of flood depth-damage curve for the data-scarce area in Malaysia S. Sulong & N. Romali 10.1080/1573062X.2022.2099292
- A new framework for flood damage assessment considering the within-event time evolution of hazard, exposure, and vulnerability T. Lazzarin et al. 10.1016/j.jhydrol.2022.128687
- Machine learning models to predict myocardial infarctions from past climatic and environmental conditions L. Marien et al. 10.5194/nhess-22-3015-2022
- Probabilistic Flood Loss Models for Companies L. Schoppa et al. 10.1029/2020WR027649
- Flood depth-damage and fragility functions derived with structured expert judgment G. Pita et al. 10.1016/j.jhydrol.2021.126982
- Bayesian Data-Driven approach enhances synthetic flood loss models N. Sairam et al. 10.1016/j.envsoft.2020.104798
- Are OpenStreetMap building data useful for flood vulnerability modelling? M. Cerri et al. 10.5194/nhess-21-643-2021
- Evaluating the spatial application of multivariable flood damage models R. Paulik et al. 10.1111/jfr3.12934
- Regional and Temporal Transferability of Multivariable Flood Damage Models D. Wagenaar et al. 10.1029/2017WR022233
- Flood Vulnerability Models and Household Flood Damage Mitigation Measures: An Econometric Analysis of Survey Data T. Endendijk et al. 10.1029/2022WR034192
- An empirical flood fatality model for Italy using random forest algorithm M. Yazdani et al. 10.1016/j.ijdrr.2023.104110
- A Consistent Approach for Probabilistic Residential Flood Loss Modeling in Europe S. Lüdtke et al. 10.1029/2019WR026213
- Expert-based versus data-driven flood damage models: A comparative evaluation for data-scarce regions M. Malgwi et al. 10.1016/j.ijdrr.2021.102148
- Invited perspectives: How machine learning will change flood risk and impact assessment D. Wagenaar et al. 10.5194/nhess-20-1149-2020
- Construction of flood loss function for cities lacking disaster data based on three-dimensional (object-function-array) data processing H. Lv et al. 10.1016/j.scitotenv.2021.145649
- A PCA spatial pattern based artificial neural network downscaling model for urban flood hazard assessment J. Carreau & V. Guinot 10.1016/j.advwatres.2020.103821
- Uncovering Drivers of Atmospheric River Flood Damage Using Interpretable Machine Learning C. Bowers et al. 10.1061/NHREFO.NHENG-1995
- The potential of open-access data for flood estimations: uncovering inundation hotspots in Ho Chi Minh City, Vietnam, through a normalized flood severity index L. Scheiber et al. 10.5194/nhess-23-2313-2023
- Evaluating adaptation measures for reducing flood risk: A case study in the city of Colombo, Sri Lanka D. Wagenaar et al. 10.1016/j.ijdrr.2019.101162
- Spatial Transferability of Residential Building Damage Models between Coastal and Fluvial Flood Hazard Contexts R. Paulik et al. 10.3390/jmse11101960
- The construction of flood loss ratio function in cities lacking loss data based on dynamic proportional substitution and hierarchical Bayesian model H. Lv et al. 10.1016/j.jhydrol.2020.125797
- Systemic Financial Risk Arising From Residential Flood Losses H. Thomson et al. 10.1029/2022EF003206
- The value of multi-source data for improved flood damage modelling with explicit input data uncertainty treatment: INSYDE 2.0 M. Di Bacco et al. 10.5194/nhess-24-1681-2024
- A machine learning-based prediction and analysis of flood affected households: A case study of floods in Bangladesh K. Ganguly et al. 10.1016/j.ijdrr.2018.12.002
- Flood Damage Assessment: A Review of Microscale Methodologies for Residential Buildings O. Aribisala et al. 10.3390/su142113817
- Enhancing resilience: Understanding the impact of flood hazard and vulnerability on business interruption and losses T. Endendijk et al. 10.1016/j.wre.2024.100244
- A Bayesian network approach for multi-sectoral flood damage assessment and multi-scenario analysis R. Harris et al. 10.1016/j.crm.2022.100410
- Understanding flood risk in urban environments: spatial analysis of building vulnerability and hazard areas in the Lisbon metropolitan area P. Santos et al. 10.1007/s11069-024-06731-w
- A data-mining approach towards damage modelling for El Niño events in Peru F. Brill et al. 10.1080/19475705.2020.1818636
- Impact of an Integrated Approach in Disaster Management O. Njoku et al. 10.4018/IJOCI.2020040102
- Improving flood impact estimations T. Sieg & A. Thieken 10.1088/1748-9326/ac6d6c
- Flood loss estimation using 3D city models and remote sensing data K. Schröter et al. 10.1016/j.envsoft.2018.03.032
- Flood Damage Analysis Using Machine Learning Techniques . Snehil & R. Goel 10.1016/j.procs.2020.06.011
- Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes N. Sairam et al. 10.1029/2019WR025068
- Validation of flood risk models: Current practice and possible improvements D. Molinari et al. 10.1016/j.ijdrr.2018.10.022
- Optimal Domain Scale for Stochastic Urban Flood Damage Assessment Considering Triple Spatial Uncertainties H. Lv et al. 10.1029/2021WR031552
- Probabilistic framework for quantifying infrastructure systems’ resilience against floods A. Daneshifar & H. Kashani 10.1080/23789689.2024.2328977
- Residential building and sub-building level flood damage analysis using simple and complex models R. Paulik et al. 10.1007/s11069-024-06756-1
- Preface: Damage of natural hazards: assessment and mitigation H. Kreibich et al. 10.5194/nhess-19-551-2019
- Flood Risk Modeling under Uncertainties: The Case Study of Croatia T. Kekez et al. 10.3390/w14101585
- Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates V. Rözer et al. 10.1029/2018EF001074
- A method to reconstruct flood scenarios using field interviews and hydrodynamic modelling: application to the 2017 Suleja and Tafa, Nigeria flood M. Malgwi et al. 10.1007/s11069-021-04756-z
- Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction D. Wagenaar et al. 10.1111/risa.13575
- Seamless Estimation of Hydrometeorological Risk Across Spatial Scales T. Sieg et al. 10.1029/2018EF001122
1 citations as recorded by crossref.
Latest update: 13 Nov 2024
Short summary
Flood damage models are an important component of cost–benefit analyses for flood protection measures. Currently flood damage models predict the flood damage often only based on water depth. Recently, some progress has been made in also including other variables for this prediction. Data-intensive approaches (machine learning) have been applied to do this. In practice the required data for this are rare. We apply these new approaches on a new type of dataset (combination of different sources).
Flood damage models are an important component of cost–benefit analyses for flood protection...
Special issue
Altmetrics
Final-revised paper
Preprint